Thanti Octavianti
Dr
Review and refine scientific analyses and findings
Empower cities to act, raise ambition, and scale implementation
Knowledge-sharing on a specific topic, method, and/or output
Awareness-raising on a specific topic, method, and/or output
This session presents findings from a landscape analysis of how AI is being applied to advance urban climate action, highlighting key trends, enabling conditions, and emerging areas of innovation. Drawing on scientific research and grey literature, and leveraging insights from C40 and GCoM, the session identifies evidence gaps and offers practical insights for city leaders, industry, civil society, and academia. It then examines a case study of Jakarta’s AI‑powered flood early warning system, focusing on responsible AI, accountability for inaccurate warnings, and how responsibilities are distributed across stakeholders. Based on surveys, expert interviews, community focus groups, and a stakeholder workshop, the case reveals high public trust, cultural nuances, and the importance of maintaining human oversight. By combining cross-sector evidence and empirical research, this session demonstrates the urgent challenge of translating global climate goals and AI applications, with their varying patterns of progress and implementation, into local and inclusive solutions.
Thanti Octavianti